Dr. van Lier-Walqui is an Associate Research Scientist and has worked at CCSR and NASA/GISS since 2013. His expertise is in using Bayesian inference methods to estimate parameters and quantify uncertainty in physical models of clouds and precipitation. This involves making comparison between observations, such as advanced polarimetric and profiling radars, and model simulations of weather. These efforts leverage the rich microphysical information content of observational systems to improve our understanding and model representation of cloud and precipitation processes. Furthermore, the Bayesian methodologies used allow for robust estimation of uncertainty that can inform forecast representations of physical process uncertainty, e.g. via probabilistic forecast ensembles.